rtvc/encoder/preprocess.py

from datetime import datetime
from functools import partial
from multiprocessing import Pool
from pathlib import Path

import numpy as np
from tqdm import tqdm

from encoder import audio
from encoder.config import librispeech_datasets, anglophone_nationalites
from encoder.params_data import *


_AUDIO_EXTENSIONS = ("wav", "flac", "m4a", "mp3")

class DatasetLog:
    """
    Registers metadata about the dataset in a text file.
    """
    def __init__(self, root, name):
        self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w")
        self.sample_data = dict()

        start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
        self.write_line("Creating dataset %s on %s" % (name, start_time))
        self.write_line("-----")
        self._log_params()

    def _log_params(self):
        from encoder import params_data
        self.write_line("Parameter values:")
        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
            value = getattr(params_data, param_name)
            self.write_line("\t%s: %s" % (param_name, value))
        self.write_line("-----")

    def write_line(self, line):
        self.text_file.write("%s\n" % line)

    def add_sample(self, **kwargs):
        for param_name, value in kwargs.items():
            if not param_name in self.sample_data:
                self.sample_data[param_name] = []
            self.sample_data[param_name].append(value)

    def finalize(self):
        self.write_line("Statistics:")
        for param_name, values in self.sample_data.items():
            self.write_line("\t%s:" % param_name)
            self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values)))
            self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values)))
        self.write_line("-----")
        end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
        self.write_line("Finished on %s" % end_time)
        self.text_file.close()


def _init_preprocess_dataset(dataset_name, datasets_root, out_dir):
    dataset_root = datasets_root.joinpath(dataset_name)
    if not dataset_root.exists():
        print("Couldn\'t find %s, skipping this dataset." % dataset_root)
        return None, None
    return dataset_root, DatasetLog(out_dir, dataset_name)


def _preprocess_speaker(speaker_dir: Path, datasets_root: Path, out_dir: Path, skip_existing: bool):
    out_dir.mkdir(exist_ok=True)
    # Give a name to the speaker that includes its dataset
    speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)

    # Create an output directory with that name, as well as a txt file containing a
    # reference to each source file.
    speaker_out_dir = out_dir.joinpath(speaker_name)
    speaker_out_dir.mkdir(exist_ok=True)
    sources_fpath = speaker_out_dir.joinpath("_sources.txt")

    # There's a possibility that the preprocessing was interrupted earlier, check if
    # there already is a sources file.
    if sources_fpath.exists():
        try:
            with sources_fpath.open("r") as sources_file:
                existing_fnames = {line.split(",")[0] for line in sources_file}
        except:
            existing_fnames = {}
    else:
        existing_fnames = {}

    # Gather all audio files for that speaker recursively
    sources_file = sources_fpath.open("a" if skip_existing else "w")
    audio_durs = []
    for extension in _AUDIO_EXTENSIONS:
        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
            # Check if the target output file already exists
            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
            out_fname = out_fname.replace(".%s" % extension, ".npy")
            if skip_existing and out_fname in existing_fnames:
                continue

            # Load and preprocess the waveform
            wav = audio.preprocess_wav(in_fpath)
            if len(wav) == 0:
                continue

            # Create the mel spectrogram, discard those that are too short
            frames = audio.wav_to_mel_spectrogram(wav)
            if len(frames) < partials_n_frames:
                continue

            out_fpath = speaker_out_dir.joinpath(out_fname)
            np.save(out_fpath, frames)
            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
            audio_durs.append(len(wav) / sampling_rate)

    sources_file.close()

    return audio_durs


def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, skip_existing, logger):
    print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))

    # Process the utterances for each speaker
    work_fn = partial(_preprocess_speaker, datasets_root=datasets_root, out_dir=out_dir, skip_existing=skip_existing)
    with Pool(4) as pool:
        tasks = pool.imap(work_fn, speaker_dirs)
        for sample_durs in tqdm(tasks, dataset_name, len(speaker_dirs), unit="speakers"):
            for sample_dur in sample_durs:
                logger.add_sample(duration=sample_dur)
    logger.finalize()
    print("Done preprocessing %s.\n" % dataset_name)


def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False):
    # preprocess train dataset
    for dataset_name in librispeech_datasets["train"]["other"]:
        # Initialize the preprocessing
        dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
        if not dataset_root:
            return

        # Preprocess all speakers
        speaker_dirs = list(dataset_root.glob("*"))
        _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir.joinpath("train"), skip_existing, logger)

    
    # preprocess dev dataset
    for dataset_name in librispeech_datasets["dev"]["other"]:
        # Initialize the preprocessing
        dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
        if not dataset_root:
            return
        
        # Preprocess all speakers
        speaker_dirs = list(dataset_root.glob("*"))
        _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir.joinpath("dev"), skip_existing, logger)
        

def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False):
    # Initialize the preprocessing
    dataset_name = "VoxCeleb1"
    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
    if not dataset_root:
        return
    
    train_dataset_root = dataset_root.joinpath("train")
    dev_dataset_root = dataset_root.joinpath("dev")

    # Preprocess train data
    # Get the contents of the meta file
    with train_dataset_root.joinpath("vox1_meta.csv").open("r") as metafile:
        metadata = [line.split("\t") for line in metafile][1:]

    # Select the ID and the nationality, filter out non-anglophone speakers
    nationalities = {line[0]: line[3] for line in metadata}
    keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if
                        nationality.lower() in anglophone_nationalites]
    print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." %
          (len(keep_speaker_ids), len(nationalities)))

    # Get the speaker directories for anglophone speakers only
    train_speaker_dirs = train_dataset_root.joinpath("wav").glob("*")
    train_speaker_dirs = [speaker_dir for speaker_dir in train_speaker_dirs if
                    speaker_dir.name in keep_speaker_ids]
    print("VoxCeleb1 train: found %d anglophone speakers on the disk, %d missing (this is normal)." %
          (len(train_speaker_dirs), len(keep_speaker_ids) - len(train_speaker_dirs)))

    # Preprocess all speakers
    _preprocess_speaker_dirs(train_speaker_dirs, dataset_name, datasets_root, out_dir.joinpath("train"), skip_existing, logger)


    # Preprocess dev data
    # Get the contents of the meta file
    with dev_dataset_root.joinpath("vox1_meta.csv").open("r") as metafile:
        metadata = [line.split("\t") for line in metafile][1:]

    # Select the ID and the nationality, filter out non-anglophone speakers
    nationalities = {line[0]: line[3] for line in metadata}
    keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if
                        nationality.lower() in anglophone_nationalites]
    print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." %
          (len(keep_speaker_ids), len(nationalities)))

    # Get the speaker directories for anglophone speakers only
    dev_speaker_dirs = dev_dataset_root.joinpath("wav").glob("*")
    dev_speaker_dirs = [speaker_dir for speaker_dir in dev_speaker_dirs if
                    speaker_dir.name in keep_speaker_ids]
    print("VoxCeleb1 dev: found %d anglophone speakers on the disk, %d missing (this is normal)." %
          (len(dev_speaker_dirs), len(keep_speaker_ids) - len(dev_speaker_dirs)))

    # Preprocess all speakers
    _preprocess_speaker_dirs(dev_speaker_dirs, dataset_name, datasets_root, out_dir.joinpath("dev"), skip_existing, logger)


def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False):
    # Initialize the preprocessing
    dataset_name = "VoxCeleb2"
    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
    if not dataset_root:
        return
        
    train_dataset_root = dataset_root.joinpath("train")
    dev_dataset_root = dataset_root.joinpath("dev")

    # Get the speaker directories
    # Preprocess all speakers
    speaker_dirs = list(train_dataset_root.joinpath("dev", "aac").glob("*"))
    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir.joinpath("train"), skip_existing, logger)
    
    # Get the speaker directories
    # Preprocess all speakers
    speaker_dirs = list(dev_dataset_root.joinpath("aac").glob("*"))
    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir.joinpath("dev"), skip_existing, logger)